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Coding in the AI Era: How to Adapt Your Working Methods in 2025

In my development practice for over 15 years, I’ve lived through several technological revolutions. But the one of artificial intelligence surpasses everything I’ve known. 84% of developers use or plan to use AI tools, according to latest studies. The AI code assistant market is exploding, going from $5.5 billion in 2024 to a forecast of $47.3 billion by 2034.

Yet, a troubling paradox emerges: if AI can generate up to 46% of code in some projects, why do 66% of developers judge this code “almost correct, but not quite”? And above all, why do 45% of them spend more time debugging than before?

The answer holds in one word: methodology.

Introduction

Artificial intelligence radically transforms how we conceive software development. But attention: this revolution doesn’t simply consist of replacing our IDE with ChatGPT. It requires a complete overhaul of our working methods.

Imagine a carpenter who would suddenly receive power tools after years of manual work. If they use them as simply faster versions of their old tools, they’ll miss their potential and might even hurt themselves. AI in development is the same: poorly used, it creates more problems than it solves.

In this article, I share lessons learned from my practical experiences and those of many developers. Because unlike marketing promises, there’s no miracle solution - only processes to build and refine.

The New Development Landscape

Explosive Growth… and Its Challenges

Numbers speak for themselves:

  • 1.5 million active users on GitHub Copilot
  • 46% of code generated automatically in enabled files
  • But also 66% of “almost correct” code according to developers

This situation reminds me of Internet’s beginnings in the 90s. Everyone knew it was revolutionary, but few truly mastered the stakes. Today, we’re living the same phenomenon with AI.

Junior/Senior Divide: An Unexpected Phenomenon

A surprising finding emerges from my observations: senior developers massively adopt AI (33% use it for over half their code), while only 13% of juniors dare take this step.

Why this disparity? Experience. A senior immediately knows when something “sounds wrong” in generated code. A junior can spend hours on a subtle bug introduced by AI.

More troubling still: juniors’ traditional training ground disappears. Those small repetitive tasks that forged reflexes? AI automates them instantly. It’s as if we removed elementary school while keeping university.

The Three Pillars of 2025 Skills

Facing this revolution, I’ve identified three essential pillars for any developer:

1. Timeless Fundamentals

Algorithms, data structures, computational thinking… These bases paradoxically become more important with AI. Why? Because they allow understanding and criticizing what AI produces.

2. AI Orchestration and Criticism

The developer becomes conductor. It’s no longer about coding, but:

  • Clearly expressing intentions
  • Dialoguing with AI
  • Testing and validating results
  • Criticizing and improving

3. Human Skills

Communication, collaboration, ethics, UX design… What AI can’t replace becomes your competitive advantage.

The PARR Methodology: Plan → Act → Review → Repeat

After many tests, I developed a structured approach I call PARR:

Plan: Engage AI in pure discussion mode. No code, just reflection. Build a detailed plan together.

Act: Request execution, but stay vigilant. At the slightest problem, stop - back to Plan.

Review: Analyze code with the same rigor as if it came from a colleague. Sometimes more.

Repeat: Iterate integrating learnings.

This method transforms AI from a simple code generator into a true development partner.

Critical Pitfalls (And How to Avoid Them)

Over-Reliance: When AI Becomes a Crutch

Symptom: Inability to solve a problem without AI assistance. Remedy: Alternate projects with and without AI. Maintain your problem-solving “muscles”.

Black Box Mentality: Accepting Blindly

Symptom: “It works, that’s enough.” Remedy: Always ask AI to explain its reasoning. Understand before accepting.

Vibe Coding: Coding “By Feel”

Symptom: Chaotic AI use without methodology. Remedy: Adopt a structured approach like PARR.

I personally experienced a case where an entire application coded with AI had major security flaws. AI had produced functional code, but vulnerable. Without critical review, the project could have been compromised.

Differentiated Strategies: Junior vs Senior

For Junior Developers

  • Review line by line all generated code
  • Decompose into tasks of maximum 30 minutes
  • Document extensively each AI block
  • Plan before prompting

AI can become your most patient mentor, but only if you constantly question it.

For Senior Developers

  • Architectural focus during review
  • Iterative approach: architecture → implementation → refactoring
  • Business validation: AI doesn’t know your business context

Conclusion

AI integration in development represents an extraordinary opportunity, but it requires a rigorous and methodological approach. Success doesn’t depend on the quantity of AI code used, but on the quality of its integration into your processes.

This revolution amplifies good practices as much as bad ones. Developers who master this human-machine collaboration will transform AI from a simple code generator into a true development partner.

The future belongs to methodical developers, not necessarily the fastest.

🎯 Ready to Transform Your Developer Career?

🚀 Complete Guide "Coding in the AI Era"

📋 Detailed PARR Framework - Proven methodology to master AI
🎯 19 pages of concrete strategies - Advanced prompt engineering techniques
🛡️ Real security cases - How to avoid security flaws in AI code
⚠️ Pitfalls to avoid - Common mistakes with AI and how to bypass them
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Article published on September 22, 2025 by Nicolas Dabène - PHP & PrestaShop Expert with 15+ years of experience in AI-assisted development

Questions Fréquentes

What percentage of developers use AI in 2025?

84% of developers use or plan to use AI tools according to latest studies. The AI code assistant market is exploding, going from $5.5 billion in 2024 to a forecast of $47.3 billion by 2034. However, only 34% truly master this collaboration effectively.

What is the PARR methodology for coding with AI?

PARR stands for Plan → Act → Review → Repeat. Plan: discuss with AI without code to build a plan. Act: request execution while staying vigilant. Review: analyze generated code rigorously. Repeat: iterate integrating learnings. This approach transforms AI from a simple generator into a true partner.

Why do senior developers adopt AI more than juniors?

33% of senior developers use AI for over half their code versus only 13% of juniors. Experience allows seniors to immediately detect when something “sounds wrong” in generated code, while a junior can spend hours on a subtle bug introduced by AI.

What are the main pitfalls to avoid with AI in development?

Over-Reliance (inability to solve without AI), Black Box Mentality (blindly accepting without understanding), and Vibe Coding (chaotic use without methodology). 45% of developers spend more time debugging AI code than before, and 66% judge this code “almost correct but not quite.”

How does AI impact junior developer training?

The traditional training ground disappears: the small repetitive tasks that forged reflexes are now instantly automated by AI. It’s like removing elementary school while keeping university. Juniors must now review line by line all generated code and extensively document each AI block.